When solar flares, disappearing filaments, and other solar events occur on the sun they create great turbulences and disturbances in the region of space close to the sun. These disturbances are often so extreme that they create shock waves which travel through space and, ultimately, arrive at the earth or at other locations of interest (e.g. a spacecraft position, a comet, or a planet), where they can cause serious problems such as loss of spacecraft, spacecraft anomalies (such as bit flips in electronic components), surface charging problems, disruption of on-board computer memories, and even damage to the structure of semi-conductor microelectronics and solar cells. The charged particles, including energetic electrons and protons, associated with these disturbances can do as much damage to solar cells and other hardware in one disturbance as several years' exposure to the undisturbed environment. For example, energetic electrons can cause large static charges, some measuring as high as 19,000 volts, to build up in insulators deep in spacecraft, which may cause arcing that damage sensitive electronic components. In addition, astronauts both inside and outside a spacecraft, space station or shuttle can be subjected to dangerous doses of protons and other types of radiation during these disturbances.
These disturbances can also cause communications blackouts at all frequencies, not only with spacecraft, but with high-flying aircraft and with ground-based objects. High frequency (HF) radio wave communication is more routinely affected since it depends on reflection from the ionosphere to carry signals great distances. Ionospheric irregularities caused by solar disturbances give rise to signal dispersion, fading, and even complete signal loss during very disturbed conditions. Ionospheric irregularities also affect the higher frequency radio waves used by telecommunication companies that penetrate the ionosphere and are relayed via satellite to other locations. The ionospheric irregularities can even prohibit critical communications such as search and rescue efforts and military operations.
One example of a serious space weather related communications failure took place in the early 1980s when President Reagan was on Air Force One on his way to China—all communications were lost with the plane for more than two hours. Mr. Reagan and his advisors were upset and concerned; they were subsequently informed that the failure was due to disturbances that originated on the sun and eventually propagated to the near earth environment.
In addition to communications systems, marine navigation systems using very low frequency signals, such as LORAN and OMEGA, depend on accurate information on the altitude of the bottom of the ionosphere. During environmental disturbances, rapid vertical changes occur in the location of this boundary, introducing significant errors of up to several kilometers in determinations of location.
Global Positioning Systems (GPS) are also sensitive to space weather disturbances. These systems have a wide variety of applications including aircraft navigation and air traffic control systems. However, because they operate by transmitting radio waves from satellites to receivers on the ground, in aircraft, or in other satellites, they are very sensitive to ionospheric disturbances. Significant errors can result when signals are reflected, refracted and slowed by disturbed ionospheric conditions.
Electric power companies are also affected by space weather disturbances because their long power lines are susceptible to electric currents induced by the dramatic changes in high-altitude ionospheric currents occurring during geomagnetic storms. Surges in power lines from ground induced currents (GICs) can cause massive network failures and permanent damage to expensive equipment. It is estimated that the March 1989 Hydro-Quebec power black-out, which was caused by a space weather disturbance, cut electric power to several million people.
With accurate early warning, spacecraft operators can take effective remedial action, such as phased shut downs of components where the most sensitive elements are turned off first and the other components are shut down closer to the predicted onset of the event. Other remedial actions include downloading spacecraft memory to ground-based memory; shutting down all spacecraft systems except those necessary for real-time tracking; increasing real-time monitoring of satellite operations for anomalies; delaying major changes in vehicle potential caused by turning on/off susceptible components; and calculating the best time to adjust a low earth orbit for drag. For military communications, redundant transmissions could be scheduled along with real-time human monitoring as a check of communication integrity. For space stations and shuttles, extra-vehicular activity could be curtailed, launches could be delayed or early landings planned to avoid a disturbance.
Such remedial actions are currently impractical due to the generally short lead time (approximately one hour) and overwhelming inaccuracy (over 80 percent false alarms) of space weather disturbance predictions. If operators were given an accurate warning at least several hours in advance of a space weather event, they would have a great deal more flexibility in developing and implementing strategies for protecting their spacecraft, systems, and/or astronauts. In addition, power companies could, for example, reduce the load on transmission circuits, confidently reset tripped protective relays on power networks, selectively ground capacitor banks to prevent large potential drops and delay power station maintenance and equipment replacement. Telecommunication companies could, for example, look for alternate frequencies for transmissions and effect plans to minimize communications outages.
The space weather forecasts provided by the National Oceanic and Atmospheric Administration's (NOAA's) Space Environment Center (SEC), the civilian office responsible for space weather forecasts, demonstrate the need for improvement that this invention addresses. Until several years ago, these forecasts were made entirely “by eye.” Operators would examine the raw data (primarily solar magnetic field, x-ray, and optical data) and then, based on intuition and experience, issue forecasts. According to the SEC's own statistics, only 30% of the storms that they forecast actually occurred. There are also many false negatives (i.e., times when they do not forecast storms that do occur) and the generally brief forecast horizon often does not provide sufficient time for effective remedial action.
Recently, others have attempted to generate more ‘objective’ forecasts based at least in part on solar wind and interplanetary magnetic field (IMF) data obtained from the Advance Composition Explorer (ACE) and the WIND spacecraft. Both these spacecraft are very close to the Earth (compared to the distance between the Earth and the sun) and therefore forecasts based on their measurements of solar wind and IMF have a very short lead time. Typically, these systems produce forecasts that have a lead time of one hour or less and often they are ex post facto (i.e. they generate a “prediction” after the event has already begun to disturb the geophysical environment).
Still other forecasting approaches rely upon data from solar event observations, inputting these data into various theoretical models that attempt to predict how the solar events, and their associated shock waves, will propagate through space and effect space weather. The Wang-Sheeley model, the Interplanetary Shock Propagation Model (ISPM) (see Dryer, M. 1998, “Multidimensional simulation of solar-generated disturbances: Space weather forecasting of geomagnetic storms,” AIAA Journal, 36, 365–370), and the Shock Time Of Arrival (STOA) model (see Smart, D. F. and Shea, M. A. 1985, “A simplified model for timing the arrival of solar flare-initiated shocks,” Journal of Geophysical Research, 90, 183–190) are examples of various theoretical models. These approaches have met with limited success due in part to the difficulty of accurately modeling the propagation of solar events through space and often in part to the lack of complete data on the solar events themselves.
It has been recognized that there is an association between SEP events and subsequent geomagnetic storms. SEPs are created when a large disturbance occurs on the sun and as the disturbance propagates through space. Some of these particles travel towards distant locations (e.g. the Earth, spacecraft, etc.) much more rapidly than the interplanetary shocks that cause many space weather events. They thus may potentially extend the space weather forecast horizon to several hours in advance of a storm and, at times, even a day or more in advance.
Past attempts to use SEPs for space weather prediction have been limited. For example, J. Joselyn described a simplistic technique for forecasting geomagnetic activity. She compared a single measure of SEP activity in only one energy channel to a set threshold. In particular, she looked at SEP events in which a flux of more than 10 protons per centimeter 2/second of energies exceeding 10 MeV (million electron-volts) occurred for at least 30 minutes; i.e., See Joselyn, J. 1995. Geomagnetic Activity Forecasting: The State of the Art. Reviews of Geophysics, 33, 3. Based on that criterion, she determined that between 1976 and 1989 such events preceded geomagnetic storms (Ap>30, where Ap is the well known global geomagnetic index) within 2–3 days 62% of the time. Joselyn also found that events with peak energetic particle fluxes exceeding 100 flux units preceded geomagnetic storms 75% of the time. Joselyn did not discuss the number or percentage of geomagnetic storms that a forecast based on such events would miss. Joselyn only compared SEP flux to a simple threshold, i.e., a single SEP data value. This simple single point comparison is too simplistic for useful prediction.
More recently, Q. Fan and J. Tian have used measures derived from two SEP values (e.g., the rise rate of SEP flux over time) as inputs to a neural network to attempt to classify the intensity of geomagnetic storms based in part on SEP data. See Fan, Q. and Tian, J. 1998, Prediction of geomagnetic storms following solar proton events (SPEs) with a back-propagation neural network, “Prediction of Geomagnetic Storms Following Solar Proton Events (SPEs) With a BP Neural Network,” AI Applications in Solar-Terrestrial Physics. Proceedings of ESA Workshop (WPP-148), edited by I. Sandahl and E. Jonsson, pp. 163–166. Each SEP (proton and electron) flux rise rate was based on only two SEP flux values, the background flux value and the peak flux value. Although Fan and Tian thus begin to recognize the value of time variations in SEP data, they, and Joselyn, failed to capture the potential of solar energetic particles as a space weather prediction tool.
Previous attempts at using SEPs in space weather forecasting have met with only limited success for many reasons. First, the prior work based predictions on only one SEP data point (a threshold or peak value) and/or measures derived from two SEP data points (such as rise rate). They therefore are not capable of identifying complex patterns in SEP data, associated with space weather events, that require consideration of three or more data points. Second, the prior work was based on analysis of only SEP data preceding space weather events, but not of SEP data preceding non-events; any system that does not take into account non-events is susceptible to false alarms and is unable to give all clear signals. Third, the prior work does not recognize the fundamental importance of recent and/or cyclical variations in SEP data (and solar, interplanetary and geophysical activity), such as variations that occur across different phases of the solar cycle. Fourth, the prior work does not provide any indication of a confidence level, such as a numerical confidence index, in a forecast. Fifth, the prior work was unable to provide a forecast while another event was in progress. Sixth, the prior work was unable to meaningfully update forecasts as new data came in.
Additionally, nearly all work prior to this invention has focused on creating prediction or identification systems based around one (or at most, several) rules or equations. Because of the highly complex nature of space weather phenomena, such simple systems are incapable of accurately modeling many of the most important aspects of space weather. Furthermore, many variables related to space weather interact to modify (or otherwise constrain) each other. Simple systems fail to take advantage of this potentially useful inter-connectedness. Because of these and other reasons, such systems have proven quite poor at generating space weather related forecasts. There is therefore a need for a space weather forecasting system that can synergistically combine separate forecasting systems and techniques.
Also, there is a need for improved notification systems for space weather events. Even the current “best” space weather forecast systems, yield only broad predictions in terms of space (e.g. North America, Alaska, or Sweden) or time (e.g. a spacecraft anomaly sometime in the next three days). Such broad predictions are of little use to most end-users. Currently, the only more specific forecasts are those that are based on ACE data (and thus have a lead-time of, at best, 45 minutes). The prior work has thus been unable to issue forecasts and/or initiate action sequences that were customized to meet the needs of end users.